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dc.contributor.advisorCarolina Osorio.en_US
dc.contributor.authorZhang, Kevin,Ph. D.Massachusetts Institute of Technology.en_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2021-01-05T23:12:45Z
dc.date.available2021-01-05T23:12:45Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/129009
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 197-203).en_US
dc.description.abstractStochastic traffic simulators are widely used in the transportation community to model real-world urban road networks in applications ranging from real-time congestion routing and control to traffic state prediction. Online calibration of these simulators plays a crucial role in achieving high accuracy in the replication and prediction of streaming traffic data (i.e., link flows, densities). In order to be relevant in a real-time context, the problem must also be solved within a strict computational budget. The primary goal of this thesis is to develop an algorithm that adequately solves the online calibration problem for high-dimensional cases and on large-scale networks. In the first half, a new online calibration algorithm is proposed that incorporates structural information from an analytical metamodel into a general-purpose extended Kalman filter framework.en_US
dc.description.abstractThe metamodel is built around a macroscopic network model that relates calibration parameters to field measurements in an analytical, computationally tractable, and differentiable way. Using the metamodel as an analytical approximation of the traffic simulator improves the computational efficiency of the linearization step of the extended Kalman filter, making it suitable for use in large-scale calibration problems. The embedded analytical network model provides a secondary benefit of making the algorithm more robust to simulator stochasticity compared with traditional black-box calibration methods. In the second half, the proposed algorithm is adapted for the case study of online calibration of travel demand as defined by a set of time-dependent origin-destination matrices. First, an analytical network model relating origin-destination demand to link measurements is formulated and validated on the Singapore expressway network.en_US
dc.description.abstractNext, the proposed algorithm is validated on a synthetic toy network, where its flexibility in calibrating to multiple sources of field data is demonstrated. The empirical results show marked improvement over the baseline of offline calibration and comparable performance to multiple benchmark algorithms from the literature. Finally, the proposed algorithm is applied to a problem of dimension 4,050 on the Singapore expressway network to evaluate its feasibility for large-scale problems. Empirical results confirm the real-time performance of the algorithm in a real-world setting, with strong accuracy in the estimation of sensor counts.en_US
dc.description.statementofresponsibilityby Kevin Zhang.en_US
dc.format.extent203 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectOperations Research Center.en_US
dc.titleReal-Time Calibration of Large-Scale Traffic Simulators: Achieving Efficiency Through the Use of Analytical Modeen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.identifier.oclc1227096827en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Centeren_US
dspace.imported2021-01-05T23:12:44Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentSloanen_US
mit.thesis.departmentOperResen_US


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